Video surveillance is of critical concern in many areas of life. One problem with video as a surveillance tool is that it may be very manually intensive to monitor. Recently, solutions have been proposed to the problems of automated video monitoring in the form of intelligent video surveillance systems. See, for example, U.S. Pat. No. 6,696,945, titled “Video Tripwire” and U.S. patent application Ser. No. 09/987,707, titled “Surveillance System Employing Video Primitives,” both of which are incorporated herein by reference. One application of video surveillance is the detection and tracking of object (e.g. human, vehicle) density. Object density refers to the apparent density of objects within an area of a scene. For example, a very sparse crowd of people on a railway platform may constitute a low density and a very dense crowd on the same platform may constitute a high density. Unfortunately, the science of computer vision, which is behind automated video monitoring, has limitations with respect to recognizing object density, such as those in subway stations, road intersections, and other object density monitoring applications.
In a high object density scene, there are two basic limitations in current video surveillance systems. One is that the standard background subtraction used in most intelligent video surveillance applications cannot be used reliably, due to the background being occluded most of the time. The other basic limitations is that one could not properly estimate object density based on counting individual objects since the heavy occlusion and very cluttered environment causes the failure of individual object tracking.